{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ❇️ Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ❇️ Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seaborn provides some builtin datasets. We will use Tips 💵 dataset today\n",
    "# to explore more try this 👉 print(sns.get_dataset_names())\n",
    "tips_df = sns.load_dataset('tips')\n",
    "tips_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ❇️ boxplot vs boxenplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "# Boxplot: The bar in middle of each box represents median value\n",
    "# and box extends from first to third quartile.\n",
    "sns.boxplot(ax = axes[0], x=\"day\", y=\"total_bill\", data=tips_df)\n",
    "\n",
    "# BoxenPlot conveys more detailed information in the tails using more quantiles, but only to a reliable extent.\n",
    "# Observations beyond the most extreme quantiles are outliers.\n",
    "sns.boxenplot(ax = axes[1], x=\"day\", y=\"total_bill\", data=tips_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ❇️ Hope you enjoyed reading!! 📖\n",
    "#### ❇️ follow → @akshay_pachaar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_twitter",
   "language": "python",
   "name": "env_twitter"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
